Classification and Segmentation of MRI Brain Images using Support Vector Machine and Fuzzy C-means Clustering

An early diagnosis of brain disorders is very important for timely treatment of such diseases.Several imaging modalities are used to capture the anomalities by obtaining either the  physiological or morphological information. The scans obtained using imaging modalities such as magnetic resonance imaging (MRI) are investigated by the radiologists in order to diagnose the diseases. However such investigations are time consuming and might involve errors. In this paper, a fuzzy c-means clustering method is used for brain MRI image segmentation.The GLCM features are obtained from the segmented images and are subsequently mapped in to a PCA space. A support vector machine (SVM) classifier is used to classify brain MRI images taken from BRATS-13 images. The method is evaluated by employing various performance measures such as  Jaccard index, Dice index, mean square error (MSE), peak signal to noise ratio (PSNR). The results show that the method outperforms the existing methods

DBI Galileon and Late time acceleration of the universe

We consider 1+3 dimensional maximally symmetric Minkowski brane embedded in a 1+4 dimensional maximally symmetric Minkowski background. The resulting 1+3 dimensional effective field theory is of DBI (Dirac-Born-Infeld) Galileon type. We use this model to study the late time acceleration of the universe. We study the deviation of the model from the concordance \Lambda CDM behaviour. Finally we put constraints on the model parameters using various observational data.Comment: 16 pages, 7 eps figures, Latex Style, new references added, corrected missing reference